A recent case study in the Journal of the American College of Radiology looked at how the Lean Six Sigma methodology can be used to improve the efficiency of an interventional radiology (IR) suite.

Lean Six Sigma is a combination of two different quality improvement approaches. The “lean” represents cutting waste, and the “six sigma” represents reducing variation to increase overall quality.

Li Zhang, PhD, from the department of diagnostic radiology at the University Hospital of Giessen and Marburg in Germany, and colleagues noticed that IR suites are consistently viewed as “bottlenecks in patient flow” that can cause delays in treatment and lead to longer hospital stays for patients.

At their own 1,200-bed academic hospital, the authors were faced with unhappy referring physicians who believed the wait times for central venous access services and percutaneous gastronomy were too long. And it seemed like a more efficient use of resources would go a long way toward solving the problem.

“Effective management of such delays can produce dramatic improvements in medical outcomes, patient satisfaction, and access to service, while reducing the costs of health care simultaneously,” the authors wrote. “At most hospitals, however, accurate data on capacity, demand, and the extent of delay are not always available, and the relationships among these individual parameters are only partly understood.”

This is when a Lean Six Sigma team was created, consisting of both radiologists and technologists. The team began by studying a five-day period in the IR suite, which is used from 8:30 a.m. to 1 p.m. for central venous access and percutaneous gastronomy.

The team made several measurements during those five days, including “process steps, times, failures, and the physical motions of technologists.” Cycle time (patient time spent in IR suite + time spent preparing IR suite for patient) and lead time (cycle time + waiting time between processes) were calculated using these measurements.

In addition, the team carried out cause-and-effect analysis, failure mode and effect analysis (FMEA) and other analyses that helped them gain a better understanding of how the IR suite could be improved.

At the end of that analysis, the team was able to step back and see why their wait times had been so slow that referring physicians felt unhappy.

“Cause-and-effect analysis and FMEA identified 69 failure modes contributing to process failures,” the authors wrote. “A total of 117 potential failure causes contributed to these modes.”

Of those 69 failure modes, the team chose 46 to focus on and made several noticeable changes to how the IR suite operates.

Preoperative preparations were moved to be performed outside of the suite, for example. And patients were provided with information sheets so that they would prepare at home as necessary. The team also refined the responsibilities of both radiologists and technologists and fine-tuned several key processes.

Finally, the team made changes to the actual procedures being performed inside the suite.

“Some steps, such as routine cardiac monitoring, were eliminated if safely feasible,” the authors wrote. “The sequence of disinfection and sterile draping was reorganized, and the positions of devices and medications were optimized to reduce the motions of IR technologists.”

Approximately ten months later, with the changes firmly in place, the team once again did a series of measurements over a five-day period.

They found that mean cycle time had decreased 28%, from 75 minutes to 53 minutes. The cycle time of steps performed by the radiologist decreased over 20%, from 29 minutes to 23 minutes.

These changes in cycle time led directly to a change in lead time, which then led to less waiting time for patients and referring physicians. The number of procedures waiting in the queue decreased 72%, from 19 procedures to less than six. The expected delay decreased 67%, from six and a half days to just over two. And these improvements were all achieved with no signs of a decrease in quality from the first five-day period to the second.

According to the authors, these statistics show that the Lean Six Sigma experiment should be viewed as a success.  

“We found in this study that the application of Lean Six Sigma improved efficiency in our IR suite,” the authors wrote. “The approach used is applicable to any IR environment because the aim of the undertaking was to eliminate process delays and failures, which are present in many IR suites. The extra capacity gained in this way has had a dramatic effect on access and on wait times.”